TransFAT: Translating Fairness, Accountability, and Transparency into Data Science Practice
Please join NYU CUSP for our new lunchtime seminar series, featuring leading voices in the growing field of Urban Informatics.
Our next seminar will feature Julia Stoyanovich, Assistant Professor, Department of Computer Science and Engineering at Tandon, and Affiliated Faculty at CUSP.
The event is open to the public. Pizza will be served!
Data science technology promises to improve people’s lives, accelerate scientific discovery and innovation, and bring about positive societal change. Yet, if not used responsibly, this same technology can reinforce inequity, limit accountability, and infringe on the privacy of individuals. In my talk I will discuss recent technical work in scope of the “Data, Responsibly” project. The goal of this project is to establish a foundational new role for database technology, in which managing data in accordance with ethical and moral norms, and legal and policy considerations becomes a core system requirement. I will connect our technical insights on fairness, diversity, transparency, and data protection to ongoing regulatory efforts in the US and elsewhere. Additional information about the project is available at https://dataresponsibly.github.io.
Julia Stoyanovich is an Assistant Professor at New York University in the Department of Computer Science and Engineering at the Tandon School of Engineering, and the Center for Data Science. Julia’s research focuses on responsible data management and analysis practices: on operationalizing fairness, diversity, transparency, and data protection in all stages of the data acquisition and processing lifecycle. She established the Data, Responsibly consortium, and serves on the New York City Automated Decision Systems Task Force, by appointment from Mayor de Blasio. In Spring 2019, Julia developed and is teaching a course on Responsible Data Science at NYU. In addition to data ethics, Julia works on management and analysis of preference data, and on querying large evolving graphs. She holds M.S. and Ph.D. degrees in Computer Science from Columbia University, and a B.S. in Computer Science and in Mathematics and Statistics from the University of Massachusetts at Amherst. Julia’s work has been funded by the NSF, BSF and by industry. She is a recipient of an NSF CAREER award and of an NSF/CRA CI Fellowship.